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Efficient by Design: Why Teams Who Eat Their Own Dog Food Win

info@holedigital.com (Hole) — Wed, 19 Nov 2025 21:45:25 GMT

BLUF | Real efficiency happens when leaders design operations with intent, align people and systems, and turn practice into progress.

Image of efficiency theater compared efficiency in action (image by J Eselgroth with Gen AI)

Why Efficiency Still Feels Out of Reach

The most effective organizations aren’t efficient by accident. They are efficient by design. They build systems that fit the way people actually work. They align data, process, and purpose so decisions flow instead of stall. Whether it’s a growing business trying to scale or a public program under pressure to deliver more with less, the goal is the same: remove friction and make every dollar count.

This year, 53 percent of leaders say productivity must increase, yet 80 percent of the global workforce say they lack the time or energy to do their work (Source: Microsoft Work Trend Index 2025 ). The demand for more output is clear, but the capacity to deliver it is stretched thin.

Too often, efficiency becomes a slogan instead of a strategy. Leaders invest in tools, launch transformation projects, and celebrate new dashboards. Then, six months later, the same bottlenecks reappear under new names.

True efficiency is not something you buy. It is something you build. It is a design choice, not a by-product.

The Efficiency Gap

Every organization measures efficiency. Few improve it. Dashboards and KPIs make it look like progress is happening, yet inside the system, work still crawls through approvals, duplicate efforts, and manual processes. It is the same story in both small businesses and large enterprises: everyone tracks activity, but few improve outcomes.

This disconnect— the efficiency gap —represents the space between performance metrics and operational reality. For small and midsize businesses, that gap eats into margins. For government and enterprise programs, it slows results and erodes trust.

Closing the gap means rethinking how we define progress. Efficiency is not about doing more work faster. It is about designing smarter systems making the right work easier to do.

The Efficiency Boom That Isn’t

Search interest for “operational efficiency” ( Google Trends - Picture below) is higher today than at any point since Google began tracking it. Everyone wants to be efficient. Yet the rise in curiosity has not produced a rise in capability.

Image of the Dog Food Index, a 2 by 2 matrix showing how practicing and talking relate (image by J Eselgroth)

In the lower right sits Efficiency Theater, the loudest voices with the least discipline. In the upper left, the Quiet Operators, teams that don’t market efficiency but embody it. And in the upper right, a rare group that walks the talk:

Amazon, where every team was required to expose its services through APIs long before “digital transformation” was trendy ( Bezos API Mandate )).
Atlassian, which runs its own operations on Jira and Confluence ( How Atlassian Uses Confluence )).
Microsoft, which acts as “customer zero” for Copilot ( Microsoft Customer Zero )).
Notion, which manages Notion on Notion ( How Notion Uses Notion ).

These organizations prove efficiency from the inside out. They don’t just measure ROI; they generate it through design. They improve by testing on themselves first. Their products work because their operations demand it. The real takeaway: efficiency isn’t about what you say, it’s about how you operate when no one is watching. That’s also called integrity.

From Burden to Advantage

For many leaders, especially in small and midsize organizations, the back office can feel like a necessary evil. Compliance, reporting, and operational overhead consume time that could be spent creating value. But what if efficiency became an advantage instead of an obligation?

Efficiency can shift from burden to advantage. Augmented intelligence makes this possible. Automation handles repetitive tasks. RPA clears routine work. Generative AI speeds documentation and insight. Predictive analytics guides decisions before issues appear. These tools create space for real thinking and real impact.

A recent Vistage study found that 63 percent of companies reporting higher productivity also saw increases in profitability, and 72 percent reported revenue growth. Efficiency doesn’t just cut costs; it creates opportunity.

This is not about replacing people. It is about amplifying them. Technology should clear the runway, not compete for control of the cockpit.

What Leaders Can Do Next

Efficiency only becomes real when leaders slow down long enough to see how work truly gets done. Intelligent transformation begins with honest reflection, not more tools or louder messaging. When leaders understand the truth of their operations, they see the patterns that move work forward and the anti-patterns that quietly slow it down. This quick scan helps teams understand where they stand and what needs to change.

Image of a runway being cleared visualizing tasks being moved out of the way (Image by J Eselgroth with Gen AI)

Efficiency as Freedom

Efficiency done well creates freedom. Freedom for teams focusing on work that matters. Freedom for leaders building without chaos. Freedom for customers and citizens receiving value without delay.

The future will not reward more dashboards or louder talking points. It will reward leaders designing operations with intelligence and care.

Efficiency is not about moving faster. It is about moving with intent. It is about finally doing what you said you would. Consistently. Clearly. With purpose. Better operations create better lives. The work improves so the person can breathe again.

Sources

Image of the topic Operational efficiency trendline on Google Trends (screenshot of Google Trends by J Eselgroth)

Recent research underscores the point. PwC found that 57 percent of operations leaders have integrated AI into at least one business function, yet 92 percent say those tech investments have not fully delivered expected results. Similarly, McKinsey reports that while 88 percent of organizations now use AI in at least one area, only about one third have scaled it successfully.

The conclusion is clear: efficiency has become a performance, not a practice. Organizations talk about it, report on it, and market around it, but few live it. The intention is real, but the follow-through is missing. The path from knowing to doing remains broken.

Who Actually Walks the Talk

Picture a simple two-by-two grid. I call it the Dog food Index. One axis shows how loudly a team talks about efficiency. The other shows how deeply it practices it.

A Simple Starting Path

Real progress begins with awareness. Leaders improve operations by seeing their system clearly, fixing obvious friction, and automating where it serves people and purpose. Testing changes on themselves first strengthens integrity and builds trust. Intelligent transformation grows through steady practice, not bigger claims.

Bending the Iron Triangle: Why Intelligent Transformation Makes Old Constraints Malleable

info@holedigital.com (Hole) — Thu, 13 Nov 2025 18:57:14 GMT

BLUF | The Iron Triangle is not dead. It becomes malleable when intelligent transformation changes the materials, not the physics. But to bend it, you must first survive the effort hump.

The Old Laws Still Echo

An old friend in firearms development reminded me recently: "Signature, lethality, mobility. Pick two." It's the weapons designer's iron triangle. Reduce signature, sacrifice mobility. Max lethality, signature grows. Another friend of mine in a different conversation reminded me: "Quality, time, features. Pick two." It's the software engineer's iron triangle. Increase features and meet quality standards, it'll take more time. Want features faster, either choose fewer features or be prepared to sacrifice quality.

We keep discovering the same constraint everywhere. But in 2025, in the age of AI, what if we can bend these laws?

The Three Mirrors

Across industries, domains, and decades, we keep running into the same immutable shape. Whether designing weapons, building software, or modernizing enterprises, leaders face a repeating pattern of constraint. Different contexts change the vocabulary, but the structure stays the same. And understanding these mirrors is the first step toward bending them.

The Iron Triangle (software): Time, Features, Quality
The Mission Triangle (weapons): Signature, Lethality, Mobility
The Transformation Triangle (AI era): Speed, Cost of Ownership, Decision Velocity

Three triangles same system of trade-offs (Image by J Eselgroth with GenAI)

One universal truth: trade-offs never disappear. But in the age of AI, their behavior changes.

Compute replaces labor.
Automation compresses time.
AI augments quality.

The constraint remains. The materials shift.

The Effort Hump

If you look at the two charts that accompany this section, you can actually see this story play out. The first curve shows the traditional digital transformation lifecycle, with its steep climb, second spike, and long tail of stabilization. The second curve overlays Intelligent Transformation on top of it and makes the contrast impossible to ignore. The shapes are familiar, but the behavior changes. The peaks flatten. The dips smooth out. The entire system becomes easier to manage.

The Digital Transformation Curve time and complexity (Image by J Eselgroth)

Transformation often begins with optimism and the promise of speed, efficiency, and improvement. Then reality arrives. The effort hump appears, the steep rise in complexity before benefits become visible. Teams retrain. Processes shift. Governance tightens. New tools collide with old patterns. This phase demands maximum cognitive load. Requirements take time. Infrastructure planning requires alignment. Testing and evaluation add weight. Security threads through everything.

In traditional transformation, a second bump follows as teams struggle to settle into new rhythms, creating friction and unplanned effort.

Now look at the Intelligent Transformation curve.

Intelligent Transformation curve, where complexity reduces and time to value increases (image by J Eselgroth)

This is where the orchestration layer truly appears. During this phase you:

Establish you approach to the 5Ps (People, Policy, Process, Partners, Platforms)
Build your Business (or Mission) Body of Knowledge
Redesign foundational workflows
Define requirements and alignment
Coordinate infrastructure planning
Test systems and integrate security
Stabilize operations and maintenance

The phases remain, but the amplitudes shrink. The first spike drops by roughly 40 percent. The O&M bump almost disappears. Modernization arrives sooner and with less strain. AI does not remove the journey. It reduces the pain . Automation absorbs repetitive tasks during planning. Machine learning accelerates validation and testing. Intelligent systems smooth the transitions that normally cause turbulence.

Key insight: Never exceed the first bump in maximum effort. Over time everything should become easier and simpler.

The gray ghost line shows the traditional path. The colored line shows intelligence bending effort itself. New materials reshape the experience, making old constraints manageable.

From Violinists to Conductors

From Violinists to Conductor (Image by J Eselgroth)

Many organizations hold modern tools but operate with old mental models. They treat AI like another instrument rather than a shift in how work happens. The old world depended on violinists. Craftsmanship lived in the hands. Quality depended on execution. Speed was limited by human capacity.

The new world needs conductors . Humans set intent, sequence, and tempo while AI-powered tools perform the heavy lift. Agentic workflows, generative models, and automated pipelines become the instruments. Humans shape the outcome.

This pivot redistributes the physics:

Speed comes from automation
Cost shifts to compute
Quality emerges from orchestration

Organizations failing to make this shift scale inefficiency. Those that do, unlock momentum and bend the Iron Triangle toward advantage.

The Super-Serum Principle

The same tension we see in the triangles shows up here too. Tools do not change your fundamentals. They exaggerate them. The same formula that created Captain America created the Red Skull . Technology amplifies what already exists.

Sloppy processes? AI scales sloppiness.
Strong standards? AI scales excellence.
Corner cutting? AI accelerates it.

Craftsmanship still determines trajectory. AI magnifies your habits, good or bad, and this is why bending the Iron Triangle requires discipline before acceleration.

Good Enough vs. Perfect

Quality becomes a managed continuum in the AI era, and leaders must rethink what progress looks like. The 80/20 rule still applies, but the rhythm changes.

AI gets you to ~80% faster, the heavy lift handled in seconds.
Humans refine the work to 90 to 95%, applying judgment, nuance, and context.
Perfection still wastes resources and almost never pays back.

"Good enough" paired with decision velocity outperforms "perfect" delivered late, every time. Momentum beats polish when polish delays impact.

This is where expectation management becomes mission critical. It is no longer a soft skill. It now sits beside prompt engineering as a foundational discipline. When expectations are unclear, quality debt accumulates fast.

Speed without quality does not create value. It just produces bad outputs quicker. The fixes' cost more. Teams burn out. Trust erodes in ways that can take years to repair.

From Triangle to Flywheel

From Iron, through malleability, and morphing into the flywheel (Image by J Eselgroth with Gen AI)

The Iron Triangle assumes static trade-offs, fixed levers you push and pull and hope to balance. Intelligent transformation changes that posture. It replaces static constraint with dynamic motion.

Think about the flywheel as the moment when a system stops resisting progress and starts reinforcing it. Each rotation builds upon the last. Each improvement strengthens the next. It is the shift from forcing progress to enabling it. Each rotation of the flywheel:

Increases decision velocity
Reduces cognitive friction
Improves outcomes
Reinforces learning

The triangle stops behaving like a box of limits and starts acting like a loop of compounding returns. It becomes dynamic, a self-improving system instead of a static set of trade-offs.

Momentum replaces trade-offs.

The Real Calculation

In my " Data-Driven Theater " analysis, a 93-slide briefing cost $241,881. The ratio was 1:3, one hour of creation for every three hours of review.

When intelligence enters the workflow, that ratio collapses. AI generates drafts in minutes. Humans validate in minutes. Creation and refinement finally move at the speed leaders expect. This is not cost-cutting. It is bandwidth reclamation. It frees teams to focus on decisions instead of production.

The entry fees remain : governance, training, integration. Once paid , returns compound quickly. Humans orchestrate. Machines execute. Work shifts from force to flow.

We turn bureaucracy into bandwidth and constraint into capability .

Transformation begins by meeting people where they are. Start with familiar constraints. Show how new materials bend them. Physics stays constant. Materials evolve.

Where You Go From Here

Your transformation either bends the Iron Triangle or it does not. If it does not, you are adding expensive technology to yesterday’s problems. Speed, cost, and quality can improve together, but only after you pay the entry fees. You must move through the effort hump, guide the human shift, and build a foundation for continuous learning.

The triangle is no longer iron. It becomes malleable when the right forces are applied. Bending takes intention. It takes discipline. It takes leadership willing to rethink how work gets done.

What's your developer-to-bureaucracy ratio? More importantly: Does your transformation bend the Iron Triangle or just paint it differently?

If you want help bending your triangle, contact me. I have spent my career helping organizations navigate these trade-offs. Weapons systems or AI systems, the physics stay the same.

You Can’t Outsource Accountability

info@holedigital.com (Hole) — Fri, 07 Nov 2025 18:46:47 GMT

BLUF | How responsible use of GenAI separates activity from intelligent action and moves you up and to the right of the digital efficiency matrix.

You can delegate tasks, but you can’t delegate responsibility. GenAI accelerates work, but accountability must remain with the professional. Intelligent Transformation isn’t about building smarter systems; it’s about ensuring that as our systems get smarter, people remain responsible for what those systems do. Technology can speed the work, but people still own the results.

From Experimentation to Ownership

Across this series, from GenAI at Work to GenAI in Action to Digital Transformation Is Dead, each step revealed a layer of Intelligent Transformation: awareness, application, and philosophy. Now comes the behavioral layer, accountability.

As AI becomes more integrated into our workflows, responsibility doesn’t shrink. It expands to include the systems we design, prompt, and oversee. The professional standard hasn’t changed; only the scale and speed of our impact have.

When Accountability Slips

Over the past year, I’ve had the opportunity to help several organizations adopt generative AI into their mission and business processes. Across these experiences, I’ve seen a familiar pattern: some people begin treating the LLM as a pass-through. The system produces content, and they accept it with little scrutiny, minor editing, light validation, and little ownership.

Quality declined. Trust eroded. Teams often fall into what I call the accountability gap , where automation runs ahead of professional judgment. On the Digital Efficiency Matrix, they tend to land in the Challenged quadrant, high digital maturity but low efficiency. Everything is digitized, but much of the work remains manual.

AI didn’t fail us; our relationship with accountability did.

Re-Centering Accountability

As a team, we reset the approach. To better understand what was happening we proactively sat down with each team member to understand their approach. Each team member walked through their process step by step. In the discussion and follow up we found three essentials for them to be successful:

Validate outputs, don’t just forward them.
Use AI to augment, not excuse.
Maintain the same quality standards as before; faster doesn’t mean shallower.

We also coached on prompting, validation, and critical review. The message was clear: GenAI supports your work, but accountability stays with you. The bar doesn’t lower because of automation, it rises.

Within the 5Ps framework, this alignment was clear:

People: Ownership and integrity
Process: Verification and iteration
Policy: Human in/on the loop governance

Accountability in Practice: Human in / on the Loop

Accountability doesn’t mean inserting humans everywhere. It means building systems that know when to bring humans back in.

Table of Human IN or ON the loop

Think of it like a control chart . As long as outputs stay within expected bounds, automation runs efficiently. When a data point drifts outside one or two standard deviations, it becomes a signal for review, an invitation for the human to step back in the loop. For a clear, hands-on example of how control limits work in practice, check out Andy Kriebel’s interactive control chart . It illustrates how variation behaves over time and how quickly you can spot signals (red dots) that fall outside acceptable limits, just as we do when monitoring the performance of intelligent systems.

Andy Kriebel's interactive control chart (Screenshot from below interactive chart)

That’s accountability at scale: professionals aren’t micromanaging every output, but they remain responsible for defining thresholds, interpreting anomalies, and acting when things fall out of bounds.

As Mark Graban reminds us in Measures of Success, “The point isn’t creating charts. It’s about wasting less time chasing ‘noise’ in our metrics, which means more time that’s available to work on systemic improvement.” These charts are not just visuals; they’re prompts for focus and intelligent response.

Or, as he also notes, “Charts like these tell us there’s a signal, it doesn’t tell us why (that’s up to us as the users of the charts).” In other words, automation can surface anomalies, but judgment still owns the why (Graban, M. Process Behavior Charts Save Us Time, Help Us Sleep Better at Night, LeanBlog.org , 2017).

Accountability isn’t about doing everything yourself; it’s about building systems that know when to bring you back into the process.

From Dependence to Mastery

Once accountability returned, everything improved. Quality rose above pre-AI levels. Turnaround time dropped from many hours to minutes. The team moved from depending on the model to mastering it.

Within the Digital Efficiency Matrix (below), this specific team’s journey moved from Challenged to Innovators , and now firmly into the Leaders quadrant, where high digital maturity meets high efficiency. This was one of many programs yet to be morphed into the Leader quadrant. For organizations wishing to pursue similar progress, the path forward begins with identifying where each program or office currently sits within the matrix and then focusing efforts on measurable accountability within that specific process area rather than trying to “boil the ocean.”

The Digital Efficiency Matrix (By J Eselgroth)

That’s how organizations scale Intelligent Transformation: not through sweeping change, but by improving individual systems and functions that collectively lift the enterprise.

Leadership lives in the top-right quadrant, where Intelligent Transformation and maximum efficiency converge.

The Leadership Imperative

Accountability is the invisible architecture of trust. You can delegate tasks, but not judgment. Systems don’t replace discernment; they amplify its consequences. Leaders must design for balance, keeping humans in and/or on the loop without reintroducing inefficiency. Intelligent oversight requires clear thresholds, transparent feedback loops, and escalation paths that work.

Delegation of tasks is smart. Delegation of judgment is dangerous.

Closing Reflection

AI can accelerate your work, but only you can validate its worth. True progress isn’t measured by how quickly we automate but by how confidently we can trust the results. Accountability turns automation into augmentation and Intelligent Transformation into trusted performance, because when professionals stay engaged, systems improve, teams mature, and outcomes sustain over time.

This article is Part 4 of the Intelligent Transformation Series.

Part 1 – GenAI at Work : Most organizations have met GenAI, but few have learned to work with it.
Part 2 – GenAI in Action : Moving from experimentation to execution with purpose and structure.
Part 3 – Digital Transformation Is Dead : Why it’s time to shift from doing digital to thinking intelligently.
Part 4 – You Can’t Outsource Accountability: Maintaining human responsibility as automation scales.

Digital Transformation Is Dead! Welcome Intelligent Transformation.

info@holedigital.com (Hole) — Fri, 07 Nov 2025 18:34:35 GMT

BLUF | Digital Transformation gave us tools. Intelligent Transformation gives us wisdom. The future isn’t about doing more digitally; it’s about deciding more intelligently.

Most organizations have spent the past decade chasing Digital Transformation. We’ve modernized systems, moved to the cloud, automated workflows, and digitized many processes that once lived on paper. We did what we needed to do… and it sort of worked.

But now, it’s time to move on. Digital Transformation isn’t failing. It’s finished. It’s run its course.

The Evolution | From Digitizing to Thinking

When Digital Transformation began, it was the right response to the problems of the time. We had paper-heavy workflows, disconnected systems, and data locked inside silos. We saw the internet, cloud computing, and automation as the tools that could finally pull us forward.

So we digitized. We turned forms into fields, meetings into dashboards, and records into databases. We became faster, more efficient, and better connected. It was exactly what we knew how to do, based on what we knew at the time. But transformation doesn’t stop when technology changes, it evolves when our understanding changes.

Today, in 2025, the boundary of “digital” has been reached. We can automate almost anything, but that doesn’t make us more intelligent. We’ve connected every system, yet we still struggle to connect decisions. The real challenge isn’t doing things faster it’s doing the right things smarter .

We’ve entered a new phase. The opportunity is to up our game moving from digitizing processes to "intelligizing" systems. Even after a decade of investment, only about 48% of digital transformation projects succeed a clear sign that the digital era has reached a plateau. Coherent Solutions, 2025

Defining Intelligent Transformation

Intelligent Transformation is about combining human judgment, machine learning, and contextual data creating systems that are adapting, learning, and improving. It’s the shift from activity to intelligence from dashboards that report the past to ecosystems that anticipate the future.

Comparing Digital to Intelligent Transformation (by J Eselgroth)

Where Digital Transformation built efficiency, Intelligent Transformation builds advantage.

Digital was about doing things digitally . Intelligent is about deciding things intelligent ly .

The rise of AI adoption underscores this shift, as an example the IT and telecom sectors alone report a 38% AI adoption rate and forecast over $4.7 trillion in gross value creation by 2035. Netguru, 2025

A Framework | The APEX Process

To help organizations move toward that next level, I use a framework called APEX , a four-stage process describing how to evolve from wherever you are on the transformation continuum toward the upper-right quadrant, where efficiency and intelligence meet. What's the efficiency matrix? Learn more in The Unending Quest for Efficiency blog article.

The APEX Process | Where insight learns. And learning becomes Advantage. (by J Eselgroth)

Each of the four stages, Architect, Position, Engineer, and eXcel , represents a disciplined action that builds toward Intelligent Transformation. What connects and empowers them all is the X , powered by the three E’s :

eXchange | the flow of data, context, and learning between systems and people.
eXponential | the compounding improvement that occurs as intelligence scales through feedback and adaptation.
eXperience | the human element that guides, governs, and trusts the system.

Together, these create the multiplier effect that transforms APEX from a process into a living framework for continuous intelligence.

Architect | Define intent, context, and purpose. Align the 5Ps (People, Policy, Process, Partners, and Platforms) to create an intelligent blueprint. This is where vision meets structure and where transformation gains clarity of direction. The Exchange begins here as insight and intent inform the design.
Position | Set the conditions for success. Translate the blueprint into readiness. Align teams, data, and technology to operate intelligently. Positioning is both strategic and systemic; it ensures governance, capability, and culture are in place before scaling. The Experience element ensures that human context and capability remain in constant dialogue as readiness evolves.
Engineer | Build with precision and intelligence. Activate the design through AI, automation, and cloud-native architectures. This is the moment when insight becomes capability, when systems learn, adapt, and deliver real-time decision support. Here, the Exponential effect accelerates learning, embedding signals from feedback loops into operations.
eXcel | Sustain and scale. Monitor, refine, and improve through feedback loops that turn data into insight and insight into continuous learning. Excel transforms transformation itself into a living, evolving capability. The three E’s operate together, multiplying impact as knowledge, trust, and performance expand.

Architect with intent . Position with intelligence . Engineer with precision . eXcel with purpose . All powered by the X , the eXchange, eXponential, and eXperience that drive Intelligent Transformation. That is the rhythm of Intelligent Transformation.

Why It Matters Now

Every organization sits somewhere on the 3×3 matrix of digital maturity and efficiency. Some are modernized but inefficient. Others are efficient but lack intelligence. Many are stuck in the middle, busy but blind. For instance, a 2025 McKinsey report shows only 28% of organizations have effectively embedded generative AI into business processes, proof that most are still bridging the gap between experimentation and integration McKinsey, 2025 .

The Digital Efficiency Matrix (left). Your nonlinear journey to being a leader (right) (image by J Eselgroth)

The good news: Intelligent Transformation isn’t linear. You don’t have to finish “digital” before starting “intelligent.” You can begin from any point (e.g. legacy, cloud, or hybrid) as long as you’re willing to connect actions to outcomes.

APEX helps organizations pivot from any position toward that upper-right quadrant, where systems are learning, decisions are accelerating, and leadership regains time for what matters most: judgment, context, and impact.

The Future | From Awareness to Advantage

Digital Transformation gave us tools. Intelligent Transformation enables us to gain wisdom. The organizations that win this decade won’t just be digital, they’ll be decisive. They’ll know how to turn information into action, how to embed learning into every decision, and how to move faster with confidence.

Transformation was about change. Intelligence is about progress.

References

GenAI in Action: 5 Steps to Move from Adoption to Advantage

info@holedigital.com (Hole) — Fri, 07 Nov 2025 18:24:54 GMT

BLUF | Turning GenAI pilots into scalable, trusted systems enhancing human productivity and organizational intelligence.

From Knowing to Doing

Most organizations have already met GenAI, but few have learned how to work with it. Across both public and private sectors, pilots are everywhere. Tools are multiplying. Yet measurable progress remains limited.

The challenge isn’t awareness. It’s application.

Real GenAI advantage comes when experimentation gives way to execution, when structure, purpose, and people align to make intelligent transformation possible.

Last time, in GenAI at Work: 5 Observations on Overcoming the Adoption Gap , I focused on what I’ve seen inside organizations wrestling with adoption. This piece builds on those lessons and shifts the lens from observation to action, from identifying patterns to creating progress.

Every successful transformation I have seen shares one truth. It doesn’t start with technology. It starts with structure.

That structure is captured in the 5Ps of Intelligent Transformation : People, Policy, Process, Partners, and Platforms. They form the foundation for every change initiative and frame how each of the five steps ahead takes GenAI from pilot to performance.

Because the goal isn’t to deploy GenAI everywhere. It’s to deploy it intelligently, in ways that strengthen how people think, decide, and deliver.

Step 1: Define the Work Before the Tool

Don’t automate confusion. Understand it first.

Every organization has hidden knowledge locked inside policies, workflows, and decisions. The first step toward intelligent transformation is documenting that knowledge so you can see where GenAI fits and where it doesn’t.

This is where the 5Ps come in. They reveal how people interact with policies, how processes depend on partners, and how platforms enable or constrain the mission.

When you map your As-Is and define your To-Be through the 5Ps lense, you create a clear blueprint for transformation. That clarity feeds your Business Body of Knowledge (BBoK) , the living record of how your organization works. It becomes the foundation that makes GenAI relevant, contextual, and trusted.

Step 2: Pair People and AI Intentionally

Match the intelligence to the task and center people in the middle of it. Not every problem needs the same kind of intelligence. Some tasks require precision and control; others need creativity and exploration.

Think of this as a triangle of capability :

On the left side is deterministic AI | predictable, rule-based systems handling structured, repeatable work.
On the right side is non-deterministic AI | adaptive, generative systems interpreting context and create new possibilities.
At the top sits people | providing direction, judgment, and validation.

The Triangle of Capability (Made with GenAI)

When these three forces work together, the organization executes work and builds capability at the same time. Deterministic systems ensure reliability and scale. Non-deterministic systems introduce adaptability and insight. People provide purpose and oversight, keeping both aligned to mission and values. This triangle creates balance. It ensures automation does not erase accountability and creativity does not override control.

The result is a human-centered system that integrates precision and imagination, scaling what works while continuously learning what could work better.

Step 3: Upskill for Collaboration, Not Compliance

Technology doesn’t replace critical thinking. It demands more of it. GenAI works best when people know how to work with it. That means helping teams understand what the tool can do, where it adds value, and how to validate its output. This is not about teaching prompts; it is about building confidence and curiosity. Upskilling the workforce is now a business imperative. Encourage employees to question, test, and iterate. Critical thinking turns GenAI from a novelty into a collaborator.

When teams see that AI amplifies rather than replaces their expertise, adoption accelerates. People begin to connect their individual contributions to the organization’s larger outcomes improving both productivity and trust.

Step 4: Measure What Matters in a Two-Way World

We are no longer giving commands to systems; we are having conversations with them. Traditional technology followed a one-way path; we input, it outputs. GenAI changes that. It is now a dialogue between human intent and machine intelligence.

Organizations must redefine what productivity means in this new relationship. Internally, that may look like reduced cycle times, faster insights, and higher-quality deliverables. Externally, it may mean better customer experiences, stronger sales, and greater innovation.

These outcomes ultimately align with two macro goals: growth and efficiency. Growth through improved products, services, and responsiveness. Efficiency through reduced rework, smarter resource use, and clearer decision-making.

The organizations measuring both will see GenAI’s true value unfold.

Step 5: Build, Measure, Learn, Rebuild, Scale

Adoption is not an event. It is an ecosystem. GenAI maturity grows through iteration. The most effective organizations establish learning loops (e.g. build, measure, learn, rebuild, scale) embedding reflection and refinement into every cycle.

Governance and experimentation must coexist. Each new use case becomes an opportunity to update the BBoK, refine the 5Ps, and strengthen alignment between people and platforms.

When adoption becomes a continuous learning system, transformation no longer depends on big launches or rigid milestones. It becomes a living process adapting as fast as the world around it.

The Learning Loop (Made with GenAI)

From Adoption to Advantage

Every organization is somewhere along this journey. Some are still experimenting. Others are scaling what works. The difference between the two isn’t budget or technology. It is structure, intention, and the discipline to learn.

GenAI doesn’t replace human intelligence; it extends it. The organizations learning how to combine both will not just move faster. They will move smarter.

Remember | Start small . Map what matters. Build your knowledge before your automation.

That is how adoption becomes advantage.

GenAI at Work: 5 Observations on Overcoming the Adoption Gap

info@holedigital.com (Hole) — Fri, 07 Nov 2025 18:17:39 GMT

BLUF | Organizations don’t fail at GenAI because of the technology—they fail because they skip the foundations of intelligent transformation.

GenAI isn’t stuck in pilot mode because it lacks capability. It’s stuck because most organizations treat adoption as a sprint, not a system. They rush to deploy tools without first aligning people, process, and purpose. Across both public and private sectors, I’ve seen that closing the GenAI adoption gap requires more than enthusiasm. It takes structure, trust, and an understanding of how humans and machines truly work together.

These five observations distill what separates experimentation from execution and how leaders can turn GenAI from an experiment into an advantage.

Observation 1: Understanding the As-Is

Before introducing AI, every organization must understand its current state. Even with digital tools, many still rely on manual synthesis, fragmented documentation, and human memory.

Using the 5Ps (People, Policy, Process, Partners, & Platforms), I’ve helped teams map workflows, stakeholders, and systems, surfacing hidden redundancies and dependencies. This exercise often leads to the creation of a Business Body of Knowledge (BBoK) , a structured repository that documents the who, what, and why behind critical activities.

The BBoK becomes the critical precursor to transformation:

It codifies tribal knowledge and clarifies business logic.
It provides context for generative AI, allowing it to understand relationships, terminology, and decisions.
It aligns people and policy around a shared understanding of the business ecosystem.

Key insight: You can’t intelligently transform what you haven’t meaningfully mapped.

Observation 2: Bringing AI to the Table

Once the foundation is in place, generative AI can join as a new kind of teammate . The most successful transformations start small, piloting use cases where AI accelerates understanding, synthesizes insights, or generates first drafts.

AI tools are guided using the BBoK. Prompts, personas, and contextual cues are refined so outputs align with business standards and tone. Each iteration strengthens the repository, creating a feedback loop of learning.

Across clients, I’ve seen the human-AI relationship follow a familiar pattern: forming, storming, norming, performing . Early trials are clumsy. Mid-way, patterns emerge. Ultimately, AI becomes a trusted collaborator, augmenting human productivity rather than mimicking it.

This isn’t automation for efficiency’s sake. It’s augmentation for adaptability and intelligence .

The GenAI Human Teaming Maturity Curve (Made with GenAI)

Observation 3: The Evolution of Team Mindset

Transformation is as much cultural as it is technical. Teams often begin by overestimating AI (“Let’s see what it can do”) or dismissing it outright. Real progress happens when they ask: “What can we do with it?”

The BBoK plays a quiet but powerful role here. By encoding business rules and relationships, it becomes a teacher to both humans and machines. Each AI-generated output is contextualized, traceable, and improvable.

Over time, skepticism gives way to exploration. I’ve witnessed light bulb moments when people see AI turn complexity into clarity or surface insights no one had noticed. The transformation becomes behavioral. Curiosity replaces caution.

Observation 4: Results and Impact

The impact across organizations is consistent and meaningful:

Cycle times drop. Tasks that once took days now take hours.
Quality improves. Consistency and accuracy rise as AI learns from human feedback.
Confidence grows. Teams begin to trust both the data and their own decisions.

The Business Body of Knowledge evolves into a living decision backbone . It’s no longer static documentation but a continuously curated ecosystem, pruned, refined, and enriched by daily interaction. Generative AI becomes the bridge connecting data, knowledge, and productivity .

The care and feeding of the BBoK become a shared responsibility. It isn’t a deliverable. It’s a living system.

Observation 5: The Bigger Lesson

Intelligent transformation isn’t about deploying AI. It’s about designing for continuous learning.

The biggest lesson: Build your foundation before you automate. A well-structured body of knowledge gives AI context. Context gives decisions meaning.

This journey requires leadership, patience, and partnership. Machines may learn faster, but humans learn wiser. The organizations that master both will outthink, not just outwork, their competitors.

The People, Policy, Proces, Partners, & Platforms Bridge from Experimentation to Execution (Made with GenAI)

FInal Thoughts: From Awareness to Advantage

Across the organizations I’ve supported, intelligent transformation has redefined what progress looks like. By combining the 5Ps evaluation factors, a living BBoK, and generative AI, they’ve turned awareness into advantage.

The true outcome isn’t speed or savings. It’s shared understanding and decision confidence .

To begin your own intelligent transformation:

Start small .
Map what matters .
Build your knowledge before your automation.

Intelligent transformation begins with curiosity and grows through care.

Data-Driven Theater vs. Data-Driven Decision Making

info@holedigital.com (Hole) — Fri, 07 Nov 2025 18:10:37 GMT

BLUF | Many organizations confuse looking data-driven with being data-driven; only genuine decision ecosystems deliver measurable impact and resilience.

How do you know whether your organization is practicing data-driven decision making...or simply performing data-driven theater? The difference matters more than ever. Despite years of investment, only 37% of enterprises describe themselves as data- and AI-driven in 2025¹. This gap shows how easy it is for organizations to fall into the trap of looking data-driven without actually becoming so. Being data-driven represents potential energy; Decision Intelligence is the actuation of that energy, turning awareness into intelligent action. The real challenge lies in moving past appearances and ensuring every data point connects directly to decisions that matter.

The Illusion: Theater

Data-driven theater thrives on optics. Organizations build dashboards, host reviews, and generate slides that look convincing but fail to guide action. Leaders may equate more slides/charts with better insight, yet numbers without context provide little direction. Vendors showcase glossy dashboards highlighting seasonal fluctuations or aggregate outputs but miss the deeper “so what” behind the numbers. Theater delivers awareness, but not accountability. It creates the feeling of progress while reinforcing cycles of performative analysis. In these environments, unresolved performance gaps linger, decisions default back to intuition, and organizations drift further from genuine improvement. Vanity measures dominate, and the harder questions, are we achieving our goals, and why or why not, go unanswered.

Case Study: The Cost of Awareness

In 2018, I was part of a large interagency team preparing a 93-slide executive briefing for a four-star general. My own contribution was only two or three slides, but the experience was eye-opening and it would turn out this wasn't a one off experience. As we moved through dry runs and prep sessions, I started wondering; how much collective time, talent, and cost went into producing this single presentation?

So, I ran the numbers.

The analysis painted a revealing picture of the hidden cost of “data-driven theater.”

Total preparation cost: $241,881.80 (2018 dollars)
Total preparation time: 772 hours — nearly five full work months
Meetings and dry runs: 14 sessions totaling $43,433
Developer-to-bureaucracy ratio: 1:3 — for every hour spent creating slides, three were spent reviewing, vetting, or updating them

The resulting deck did its job, it informed senior leadership and reflected the organization’s current status. Yet it was fundamentally a rearview-mirror exercise , focused on awareness rather than decision-making. It offered insight into what had happened, not what should happen next.

That realization marked a turning point for me. The effort, while well-intentioned, revealed the structural inefficiency of many “data-driven” processes: enormous energy devoted to building artifacts of understanding, but little investment in converting those artifacts into action. The real opportunity wasn’t in making better slides...it was in designing better systems for translating data into decisions.(image generated with AI)(image generated with AI)

The Reality: Decisions

Breaking free from theater requires a shift from demonstration to deliberation. Real data-driven decision making begins with a clear decision statement: what must we decide, why now, and what success looks like. From there, organizations build reliable pipelines and embed feedback loops to test whether actions change outcomes. Consider a small defense agency, which built forecasting models aligning missions, funding, and identification priorities. Leaders moved from manual data pulls and Excel with static slides to live data feeds, enabling real-time prioritization of global recovery operations. Or the Air Force, which developed a $954M, 30-year range modernization strategy by integrating cost, mission, safety, and joint-service priorities. This effort represented an early form of Decision Intelligence (DI)...integrating diverse data sources and foresight modeling to guide long-term planning. These cases show what is possible when organizations stop admiring dashboards and start embedding data into their operating rhythm building an action-to-outcome DI process. Technology alone does not create the difference; intentional strategy and accountability do.

Spotting Substance

Leaders can tell the difference by asking: is this a stat, a metric, or a performance measure? Stats describe, metrics compare, and performance measures drive action over time. When leaders see a number, they should be able to connect it to a decision, an accountable owner, and a timeframe for change. Think of data and decisions as two sides of the same coin. One side shows the numbers and metrics, the visible data. The other side reflects the confidence, quality, and governance that make those numbers trustworthy. For instance, control charts help distinguish signal from noise, ensuring leaders do not overreact to normal fluctuations. Equally important, confidence in decisions depends on confidence in data quality, the other side of the coin. Without governance, metadata, and validation, even the best-looking metric erodes trust. Dashboards failing to answer “what happens next” are theater. Dashboards highlighting gaps, enabling response, and tracking improvements are substance. These capabilities form part of the foundation for Decision Intelligence, where data meets intent through structured reasoning and feedback.

A dual-sided emblem of trust and logic, where confidence meets data-driven decision-making (image generated with AI)

The Digital Efficiency Journey

Authentic change means embracing intelligent transformation, where data and decisions move together across the enterprise. The journey toward the Leader box in the digital efficiency matrix (learn more about the matrix here The Unending Quest for Efficiency: Navigating Beyond Digital Transformation ) emerges when organizations minimize theater and maximize impact, ensuring insights produce outcomes improving missions, operations, or customer experiences. The journey can be framed through the 5Cs of Intelligent Transformation:

Cognition : Define outcomes, decision rights, and guardrails, aligning augmented intelligence with strategy and objectives.
Capability : Develop people and leadership skills, creating pathways that embed competency and sustain improvement.
Culture : Reinforce behaviors, incentives, and adoption patterns that make data-driven habits part of everyday work.
Connectivity : Integrate data sources, platforms, processes, and workflows, enabling interoperability and automation across functions.
Continuity : Establish governance, risk, and resilience, embedding ethics, security, and continuous improvement into decisions.

Purpose: turn data, AI, and operations into reliable decisions across the lifecycle. Outcome: insight amplified, decisions empowered. The 5Cs provide a practical compass for leaders who want to move beyond vanity dashboards toward measurable transformation, and together, they serve as an operating model for implementing Decision Intelligence at scale.

Leadership Imperative

Data theater is not a method. It dazzles but rarely delivers measurable improvements or enduring organizational learning. General (ret.) Gordon R. Sullivan, in his book Hope Is Not a Method (1997), emphasized that hope is not a method². The same lesson applies here: theater cannot replace genuine strategy. Leaders must identify evangelists, launch manageable initiatives, and build momentum through iteration. Each cycle compounds, shifting culture from reactive demonstration to proactive decision-making. Decision Intelligence transforms theater into traction, embedding forecasting, scenario planning, and consequence modeling directly into leadership forums. Evidence must replace entertainment, and courage must replace complacency. Theater entertains, but decision-making wins campaigns. Organizations that separate optics from impact build resilience, trust, and enduring strategic advantage.

So, ask yourself: What’s our developer-to-bureaucracy ratio?

References

MIT Sloan Management Review. Five Trends in AI & Data Science for 2025 by Randy Bean. Link Used for: 37% of enterprises describe themselves as data- and AI-driven in 2025.
Sullivan, Gordon R. Hope Is Not a Method: What Business Leaders Can Learn from America’s Army. 1997. Link Used for: “Hope is not a method” leadership lesson.

Bridging the Gap: Turning Your Vision into Reality

info@holedigital.com (Hole) — Fri, 07 Nov 2025 18:05:55 GMT

BLUF | Bridging the gap between strategy and execution requires clarity, alignment, and data-driven action to turn vision into results.

The world of business of government is littered with well-intentioned strategies that never see the light of day. The idea that 'strategic planning is dead' caught my attention in a recent Fast Company article by Lisa Bodell[1]. It got me thinking about the disconnect between strategy and execution. But why? In addition to Lisa’s points, it's not because strategy itself is flawed, but rather the execution that often falls short. The gap between a brilliant idea and tangible results can feel like an insurmountable chasm.

Here are some statistics from Lisa Bodell’s article:

48% of all organizations fail to meet at least half of their strategic targets.
61% of C-level executives acknowledge their firms struggle to bridge the gap between strategy formulation and day-to-day implementation.
47% of team members don’t know how their leadership is tracking the strategy execution.

But what if it doesn't have to be? What if turning strategy into reality is less about grand pronouncements and more about understanding the mechanics of action? The ability to connect the high-level vision to the boots-on-the-ground implementation is where the true magic happens. It's about understanding how the 'rubber meets the road' and propels the organization forward.

Let’s explore how to bridge that gap and delve into the common pitfalls that derail even the best-laid plans. Then provide practical steps to ensure your strategies don't just gather dust on a shelf. I'll draw upon my own experiences, which include the successful transformation of Air Force small arms firing ranges and the creation of a unified executive decision support system. These insights will provide you with actionable tips to empower you to drive real change.

Whether you're a seasoned executive or an aspiring leader, this guide equips you with the tools and mindset to move beyond the talk and into the realm of tangible results. It's time to turn your strategies into reality.

The Disconnect: Why Strategies Fail

Before we dive into the solutions, let's examine why so many strategies falter. The reasons are multifaceted, but some common culprits include:

Lack of Clarity: The strategy itself may be vague or poorly defined, leaving teams unsure of how to proceed.
Misalignment: Different departments or individuals may have conflicting interpretations of the strategy or prioritize their own goals over the overarching vision.
Insufficient Resources: The strategy may require resources (financial, personnel, technological) that are not readily available.
Resistance to Change: People are naturally resistant to change, and a new strategy can disrupt established routines and power structures.
Failure to Measure Progress: Without clear metrics and regular monitoring, it's impossible to know if the strategy is on track or needs adjustments.
Confusing Motion with Productivity: The hustle and bustle of meetings, discussions, and busy work can create an illusion of progress. However, true productivity lies in tangible outcomes and measurable results.

Don't Confuse motion with productivity (Image generated with Gemini)

By recognizing these pitfalls, leaders can proactively address them and increase the likelihood of successful strategy execution.

Bridging the Gap: A Framework for Success

Turning strategy into reality requires a systematic approach that addresses these challenges. Here's a framework to guide you:

Clarity is king (Image generated with Gemini)

Clarity is King: A crystal-clear strategy with well-defined goals, objectives, and KPIs is essential. Everyone involved should understand the 'why' and their role. This alignment fosters ownership and empowers individuals to drive the strategy forward.
Align and Communicate: Foster a culture of open communication and collaboration. Break down silos and ensure everyone is working towards the same goals. Regularly communicate progress and address any concerns or roadblocks. Radical transparency is paramount.
Resource Strategically: Conduct a thorough assessment of the resources required to implement your strategy. Understand the people, policy, process, partners, and platforms (5Ps) that are related to the strategy. Secure the necessary funding, personnel, and technology upfront, or develop a plan to acquire them as needed.
Embrace Change Management: Recognize that change is a process, not an event. Anticipate resistance by understanding the 5Ps mentioned earlier and proactively address it through clear communication, training, and support. Celebrate early wins to build momentum and enthusiasm.
Measure, Monitor, and Adapt: Establish clear metrics to track progress and regularly review performance data. Be prepared to adjust your strategy as needed based on feedback and changing circumstances.

Actionable Tips for Leaders

Embrace a Systems Thinking Approach: Understand how different parts of your organization interact and impact each other. Recognize that even small actions can have ripple effects.
Adopt a Decision Driven Data Mindset: Base your decisions on evidence and insights, not just gut feelings. Leverage data to track progress, identify roadblocks, and make informed adjustments.
Foster a Culture of Transparency and Communication: Break down silos and encourage open dialogue. Ensure everyone understands the strategy and their role in its execution.
Start Small and Iterate: Don't try to tackle everything at once. Begin with a pilot project or a specific area where you can demonstrate success and build momentum.

Start small and iterate, from Idea to full maturity (Image generated with Gemini)

Celebrate Wins and Learn from Setbacks: Acknowledge progress and recognize the efforts of your team. When things don't go as planned, analyze what happened and use those lessons to improve.
Lead by Example: As a leader, your actions speak louder than words. Demonstrate your commitment to the strategy and inspire others to follow suit.
Cultivate a Culture of Risk-Taking: Encourage experimentation and innovation. Celebrate both successes and well-intentioned failures. Publicly acknowledge those who take risks, even if the outcome isn't perfect. This fosters an environment where people feel safe to share ideas and push boundaries, ultimately driving progress and growth.

My Personal Journey: From Data Silos to Strategic Action

My journey towards turning strategy into reality has been shaped by diverse experiences, each offering unique lessons. One particularly impactful endeavor involved supporting a small defense organization in their quest to streamline decision-making and enhance data utilization.

Situation: The organization, like many others, grappled with siloed data sources and a lack of cohesive insights to inform strategic decisions.
Task: As a trusted advisor to the Chief Data Officer, I led a team that was tasked with developing and implementing a comprehensive data strategy to address these challenges.
Action: We spearheaded the integration of six disparate data sources into a unified executive decision support system, aptly named the "common operating picture." This involved close collaboration with stakeholders across departments to understand their specific data needs, designing a scalable data architecture, and implementing data pipelines for real-time insights. Recognizing the critical importance of data quality for confident decision-making, we also developed the organization's first data health dashboard to provide transparency into data integrity.
Result: The common operating picture dramatically reduced the time required for information gathering, empowering senior leaders to make faster, more informed decisions. The data health dashboard fostered trust in the insights generated from the system, further enhancing the effectiveness of the decision-making process. This initiative not only improved efficiency but also had a ripple effect, benefiting the organization's customers and stakeholders.

Beyond this specific project, my journey has been marked by a commitment to developing decision intelligence strategies and tools, fostering digital and data literacy, and championing agile DataOps processes. These experiences have reinforced the transformative power of data-driven approaches in turning strategy into tangible outcomes.

Final Thoughts

Turning strategy into reality is not a mystical art reserved for a select few. It's a discipline that can be learned and mastered. By embracing a systematic approach, fostering a culture of collaboration, and leveraging data-driven insights, you can bridge the gap between vision and execution.

Remember, the journey from strategy to reality is not always linear. There will be challenges and setbacks along the way. But with perseverance, adaptability, and a commitment to continuous improvement, you can achieve remarkable results.

So, take that bold step. Embrace the challenge. Turn your strategies into reality and lead your organization to new heights of success.

[1] https://www.fastcompany.com/91176813/strategic-planning-is-dead-this-is-why-we-need-to-revive-it

The Unending Quest for Efficiency: Navigating Beyond Digital Transformation

info@holedigital.com (Hole) — Fri, 07 Nov 2025 17:30:18 GMT

BLUF | Digital transformation is not the finish line. True leaders pursue continuous improvement through efficiency, innovation, and adaptability.

The year 2013 marked a turning point in the business world with the introduction of the term "digital transformation." 1 Over a decade later, a striking “70% of companies either have a digital transformation strategy in place or are actively working on one” 2 . However, as the digital landscape becomes increasingly saturated, simply having a strategy is no longer enough. Organizations must go beyond initial adoption and focus on continuous innovation and optimization to extract maximum value from their investments. Even for the “21% of companies that believe they've completed their digital transformation” 3 , the journey is far from over. The question for these organizations is not 'Are we done?' but rather 'What's next?' The focus must shift from achieving a static state of digital maturity to embracing a mindset of continuous evolution and adaptation.

The Digital Efficiency Matrix: A Roadmap for Progress

To understand where we stand and where we can go next on this journey, we introduce the Digital Efficiency Matrix. This 3x3 grid provides a comprehensive view of an organization's digital maturity and efficiency, guiding us toward the ultimate goal of becoming leaders in our respective industries.

Understanding the Axes

Efficiency (X-Axis): This axis measures how effectively an organization utilizes its resources (time, money, labor, etc.) to achieve its goals. It ranges from low efficiency, characterized by high manual labor, low compute power, and high costs, to high efficiency, marked by minimal manual labor, extensive compute power, and lower costs.

Digital Maturity (Y-Axis): This axis represents the extent to which an organization has adopted and integrated digital technologies into its operations. It spans from paper-based processes to intelligent/autonomous systems that leverage AI and machine learning.

The Journey So Far: The Inner 2x2 Matrix

Traditionally, organizations have been assessed using a simpler 2x2 matrix, focusing on basic levels of digital maturity and efficiency. This matrix identified four quadrants:

Laggards: Low efficiency, low digital maturity
Stragglers: Low efficiency, moderate digital maturity
Experimenters: Moderate efficiency, low digital maturity
Implementers: Moderate efficiency, moderate digital maturity

The Path Forward: The Expanded 3x3 Matrix

The expanded 3x3 matrix offers a more nuanced perspective, revealing new possibilities and potential paths for organizations to advance:

Early Adopters: Moderate efficiency, high digital maturity
Challenged: Low efficiency, high digital maturity
Streamliners: High efficiency, moderate digital maturity
Innovators: Moderate efficiency, high digital maturity
Leaders: High efficiency, high digital maturity

Charting Your Course: Where Are You Now, and Where Can You Go?

The first step in any successful strategy is understanding your current position. By identifying your organization's location within the Digital Efficiency Matrix, you can gain valuable insights into your strengths and weaknesses and chart a course toward greater efficiency and digital maturity.

Descriptions and Strategies

Laggards: Organizations in this location are heavily reliant on manual, paper-based processes. They often experience high operational costs, slow turnaround times, and a higher risk of errors due to the manual nature of their work. To progress, they should focus on basic digitization efforts to kickstart their digital journey.
Experimenters: These organizations are taking their first steps into the digital realm, experimenting with new tools and processes. While they are making some progress, their efforts are often scattered and not fully integrated. Investing in digital tools and training can help them improve efficiency and move towards higher digital maturity.
Early Adopters: These organizations have embraced digital technologies early on but may lack a cohesive strategy. They are seeing positive results in specific areas but have the potential to achieve even greater efficiency by expanding their digital initiatives and developing a comprehensive digital strategy.
Stragglers: These organizations have adopted some digital tools but are not using them effectively, leading to limited efficiency gains. This could be due to a lack of training, poor implementation, or resistance to change. To overcome these challenges, they should focus on optimizing processes and enhancing their digital capabilities.
Implementers: These organizations are systematically implementing digital technologies and are seeing moderate improvements in efficiency. To maintain momentum, they should continue to refine and expand their digital initiatives.
Streamliners: These organizations have well-integrated digital systems and automated workflows, leading to high efficiency. They use data to make informed decisions and continuously optimize their processes. To further advance, they can enhance digital innovation and explore emerging technologies.
Challenged: These organizations possess advanced technology but face challenges in implementation and achieving efficiency. Addressing inefficiencies by optimizing digital tools and processes is key to unlocking their full potential.
Innovators: Organizations in this category exhibit high digital maturity and moderate efficiency. They are innovative but may need to enhance efficiency by focusing on scaling innovations and improving process efficiency.
Leaders: These organizations are at the forefront of digital transformation and efficiency, leading in both digital innovation and process optimization. To maintain their leadership position, they must continue to push boundaries, set benchmarks, and embrace a mindset of continuous evolution and adaptation.

Overcoming Challenges and Embracing Continuous Improvement

While the journey towards digital maturity and efficiency is promising, it's not without its challenges. Organizations may encounter obstacles such as resistance to change, a lack of skilled talent, and difficulty measuring ROI. Strong leadership, a clear vision, and a commitment to continuous improvement are essential for overcoming these hurdles and achieving long-term success. (read about why the 5Ps are essential to Digital Excellence )

The Role of Leadership

Leaders play a crucial role in driving digital transformation and fostering a culture of continuous improvement. They must champion the adoption of new technologies, invest in employee training and development, and create an environment that encourages experimentation and innovation.

The Power of Continuous Improvement

In the dynamic digital landscape, there is no finish line. Organizations must embrace a mindset of continuous improvement, constantly seeking ways to optimize processes, leverage new technologies, and enhance efficiency. By doing so, they can stay ahead of the curve, adapt to changing market conditions, and achieve sustainable growth.

Conclusion

The pursuit of efficiency is an ongoing journey, not a destination. While digital transformation has been a significant step for many organizations, it's essential to recognize that the quest for improvement never ends. By leveraging the Digital Efficiency Matrix, organizations can gain a clearer understanding of their current state, identify areas for growth, and develop strategies to achieve higher levels of digital maturity and efficiency. With a focus on continuous innovation and adaptation, organizations can navigate the complexities of the digital age and thrive in the years to come.

Footnote

2013 The term “Digital Transformation” is coined ( Hatchworks )
70% of companies either have a digital transformation strategy in place or are working on one. ( Tech Pro Research )
21% of companies believe they’ve completed their digital transformation. ( Forrester )

Decision Intelligence: The Missing Piece in AI Orchestration?

info@holedigital.com (Hole) — Fri, 07 Nov 2025 17:09:21 GMT

BLUF | Decision Intelligence bridges data, technology, and human insight to orchestrate AI effectively, transforming fragmented efforts into real-world outcomes.

In the world of AI, "Most AI projects fail. Some estimates place the failure rate as high as 80%" . Simultaneously, the buzz around Decision Intelligence (DI)* is growing . Could DI be the key to AI orchestration? In my view, the answer is both yes and sort of.

AI orchestration, while promising, presents a myriad of challenges. On the technical side, the complexity of managing diverse AI components, ensuring scalability with growing data volumes, and integrating with existing systems can be daunting. On the human side, there are skill gaps in AI and software engineering, collaboration difficulties between teams, resistance to change due to workflow disruptions, and ethical considerations around data privacy and bias.

The Case for DI

To the "yes", DI brings together the often-disparate elements of data and technology into a unified strategy. It addresses the human element – the "squishy things" like subjectivity and intuition – that are often overlooked in AI implementations. Think of DI as the glue that holds everything together, focusing our efforts on making sense of the data available to us and transforming it into improved outcomes. When faced with the challenge of finding a needle in a haystack, DI helps us "burn the hay" – eliminating irrelevant data and focusing our efforts on the most valuable information. In essence, DI helps us cut through the noise and focus on finding the right data.

When trying to find the needle (the right data) in the haystack, we need to ask ourselves “what if we burned the hay?” (image developed with DALL-E)

However, to the "sort of", implementing DI isn't as simple as plugging it in and expecting magic. It requires a holistic approach, what I call the "digital transformation (DT) of becoming a data-driven decision organization." This means considering the impact on people, policy, process, partners, and platforms – the 5Ps.

The 5Ps: A Framework for Bridging AI Orchestration Challenges to Impactful Outcomes with DI

The 5Ps framework, when applied through the lens of Decision Intelligence (DI), can effectively bridge the gap between AI orchestration challenges and impactful organizational outcomes:

People: DI empowers individuals by fostering a data-driven culture and enhancing decision-making skills. By investing in training and development, organizations can cultivate a workforce proficient in both AI and software engineering, mitigating the skills gap and fostering collaboration. This leads to improved communication, streamlined workflows, and reduced resistance to change, ultimately driving higher adoption rates and successful AI implementation.
Policy: DI-driven policies establish clear ethical guidelines for AI, ensuring responsible and unbiased decision-making. By defining roles and responsibilities, organizations can create a governance framework that promotes transparency and accountability, mitigating risks and building trust in AI-powered systems. This results in ethical AI practices, reduced legal and reputational risks, and increased stakeholder confidence.
Process: DI enables the design and optimization of processes that seamlessly integrate AI into existing workflows. By leveraging change management principles and involving employees in the process, organizations can minimize disruptions and ensure smooth transitions. This leads to increased efficiency, reduced errors, and improved decision-making across the organization.
Partners: DI facilitates effective collaboration with internal and external partners, leveraging their expertise to overcome technical challenges and accelerate AI implementation. By identifying and managing partners, organizations can access specialized skills, knowledge, and resources, leading to faster time-to-market, reduced development costs, and innovative solutions.
Platforms: DI guides the selection and implementation of scalable and flexible AI platforms that align with organizational needs. By ensuring compatibility with existing systems and incorporating robust monitoring and maintenance tools, organizations can maximize the value of their AI investments. This results in improved performance, enhanced scalability, and the ability to adapt to evolving business requirements.

From Lab to Reality: Bridging the Gap

While the 5Ps framework provides a solid foundation for addressing AI orchestration challenges, it's important to acknowledge the gap between theory and practice. Many AI initiatives that show promise in the lab fail to deliver the expected results in real-world scenarios. This can be due to various factors, such as unforeseen technical issues, organizational resistance, or a lack of understanding of the specific business context.

To bridge this gap, organizations can leverage DI to create a feedback loop that continuously improves AI models and their real-world performance. By applying DI principles, businesses can identify and rectify biases, refine algorithms, and ensure that AI systems align with evolving business objectives. This iterative process not only enhances the accuracy and effectiveness of AI but also fosters trust and transparency, crucial factors for successful AI adoption.

Start Small, Win Big

To win over leadership and ensure successful DI adoption, I advocate for a "start small" approach. Begin with small, impactful MVPs (Minimum Viable Products) that demonstrate the value of DI in real-world scenarios. These early wins can build momentum and pave the way for wider adoption. Find one problem the organization has been trying to answer, and start there. Focusing on a single problem instead of trying to boil the ocean allows you to learn and finetune before doing more. The momentum from, hopefully, a successful DI implementation encourages others to participate and simultaneously improve leaderships confidence DI. After all, as I discovered in my master years ago, in order for change to be accepted by all, it must start off small.

AI orchestration (image developed with DALL-E)

"In order for change to be accepted by all, it must start off small" - James Eselgroth

Final Thoughts

Decision Intelligence has the potential to revolutionize AI orchestration. By focusing on the right data and "burning the hay" of irrelevant information, DI empowers organizations to make better, faster, and more informed decisions. But to realize its full potential, organizations need to take a holistic approach, considering the 5Ps and fostering, as Mark Zangari mentioned to me recently, a "decision driven data culture." Remember, start small, win big – and don't underestimate the power of people in the success of your DI journey

*"Decision intelligence (DI) is a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback." - Gartner Glossary

Augmented Intelligence: Leveraging Technology like a Superpower

info@holedigital.com (Hole) — Fri, 07 Nov 2025 16:38:37 GMT

BLUF | Augmented Intelligence fuses human creativity with machine precision to enhance decisions, amplify insight, and drive smarter outcomes.

Over the last decade, and especially the last 18 months, artificial intelligence (AI) has been heralded as the next great technological revolution. From self-driving cars to virtual assistants, AI promises to transform nearly every aspect of our lives. However, as impressive as modern AI systems are, they still face significant limitations. Limitations such as understanding context, inability to reason, lacking emotional intelligence, and developing strategy. That's where the concept of augmented intelligence comes into play – an approach that combines the strengths of humans and machines to achieve something greater than either could accomplish alone.

What is Augmented Intelligence?

At its core, augmented intelligence is about enhancing human intelligence rather than replacing it. Instead of attempting to create artificial general intelligence that can match or surpass human cognition, augmented intelligence systems are designed to complement and extend our natural capabilities. These systems leverage advanced algorithms, massive datasets, and immense computational power to process information in ways that would be impossible for the human mind alone.

One of the key advantages of augmented intelligence is its ability to handle complex, data-intensive tasks with incredible speed and accuracy. For example, an augmented intelligence system could rapidly analyze millions of medical records, identifying patterns and insights that could lead to breakthrough treatments or more effective preventative care strategies. Another example, you upload a large document into a Large Language Model (LLM) like ChatGPT, Gemini, Claude, or another one and you start asking questions about the document. The LLM parsed and analyzed the document for you, in seconds, so you can ask it questions. This Saves you time, improves your understanding, and probably improves your productivity.

Augmented Intelligence: Where Human Ingenuity Meets Machine Precision (Image created with DALL-E 3)

Augmented Intelligence in Action: Decision Intelligence

One area where augmented intelligence is already making a significant impact is in the field of decision intelligence. Decision intelligence combines various disciplines, including data science, decision analysis, behavioral science, and AI, to help organizations make better, more informed decisions.

Decision intelligence starts by understanding the decision at hand, breaking it down into its core elements: actions, intermediates, externals, and outcomes. This systematic approach ensures that all relevant factors are considered, from the potential choices that can be made (actions) to the dependencies and consequences of those choices (intermediates) as well as external forces and constraints beyond the organization's control aimed at a desired outcome.

At its core, decision intelligence bridges the gap between the non-material aspects of an organization (e.g. people, strategy, doctrine, policy) and the material aspects (e.g. hardware, software, data, analytics, AI). By leveraging augmented intelligence techniques, decision intelligence systems can provide decision-makers with highly accurate predictions, simulations, and recommendations based on vast amounts of data and complex modeling.

The Power of Human-Machine Collaboration

One of the most exciting aspects of augmented intelligence is the potential for true collaboration between humans and machines. While AI systems excel at processing vast amounts of data and performing complex calculations, humans possess unique abilities in areas such as creativity, emotional intelligence, and strategic thinking.

“Computers are great at patterns, humans are great at strategy” - David Epstein, Range

By combining these strengths, augmented intelligence systems can achieve incredible results. Humans can provide the high-level goals, context, and intuition, while the machine learning algorithms and computational power of the augmented intelligence system can rapidly explore countless scenarios and provide data-driven recommendations.

This collaborative approach not only leads to better outcomes but also helps to build trust and acceptance of these advanced systems. When humans are actively involved in the decision-making process, rather than being replaced by opaque "black box" AI, they are more likely to understand and embrace the insights and recommendations provided by the augmented intelligence system.

You're probably already living with augmented intelligence in your day-to-day life right now without even realizing it. Any guesses? If you use Waze, Google Maps, Apple Maps, or any other GPS navigator, you're taking information from a complex supercomputer powered by billions if not trillions of data points to provide you with a recommended path to your destination. But you are in the driver's seat – the computer is providing you, the human, a recommendation based on current conditions. You can either choose to follow it or not. And if you're like me, sometimes when you override the recommendation, you're stuck in really bad traffic. And you think to yourself, "I knew I should've followed Waze."

Leveraging Augmented Intelligence in our daily lives Using a Navigation device (Image created with DALL-E 3)

As we continue to push the boundaries of what's possible with technology, it's becoming increasingly clear that augmented intelligence, rather than pure artificial intelligence, may be the true superpower of the future. By combining the complementary strengths of humans and machines, we can tackle challenges that would be insurmountable for either alone, unlocking new levels of innovation, productivity, and understanding.